scholarly journals Dependence, trust, and influence of external actors on municipal urban flood risk mitigation: The case of Lomma Municipality, Sweden

2018 ◽  
Vol 31 ◽  
pp. 1004-1012 ◽  
Author(s):  
Per Becker
2018 ◽  
Vol 144 (10) ◽  
pp. 05018013 ◽  
Author(s):  
Fabio Scionti ◽  
Marcelo Gomes Miguez ◽  
Giuseppe Barbaro ◽  
Matheus Martins De Sousa ◽  
Giandomenico Foti ◽  
...  

2015 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Antje Witting ◽  
Deborah Kallee

2021 ◽  
Vol 3 ◽  
Author(s):  
Carlotta Quagliolo ◽  
Elena Comino ◽  
Alessandro Pezzoli

Cities are vulnerable to extreme weather events, particularly by considering flash flood risk as a result of even more short-duration intensive rainfall. In the context of climate change, compound flooding due to simultaneous storm surges and increased runoff may further exacerbate the risk in coastal cities, and it is expected to be frequent and severe across several European urban areas. Despite this increasing evidence, the spatial knowledge of the hazardous events/vulnerabilities through modelling scenarios at the urban level is quite unexplored. Moreover, flood-prone areas often do not correspond to the traditional flood risk classification based on predicted return-period. The result that huge impacts (human losses and damages) occur everywhere throughout the city. Consequently, this new challenge requires stormwater flooding mitigation strategies to adapt to cities while mainstreaming urban flood resilience. In this paper, we considered the Urban Flood Risk Mitigation model through the employment of the open-source tool—Integrated Evaluation of Ecosystem Services and Trade-off (InVEST)—developed by the Natural Capital Project, integrated into a GIS environment. The model application in the three urban coastal territory of the Liguria Region (Italy) estimated the amount of runoff due to two extreme rainfall events for each watershed considered. These index calculation results help define examples of Natural Water Retention Measures (NWRM) per land-use type as resilient solutions by addressing site-specific runoff reduction. Local sensitivity analysis was finally conducted to comprehend the input parameter's influence of rain variation on the model.


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